Data validation & verification
Just a heads-up—this section is still a work in progress! I’ll be revising and expanding it soon to make sure it’s as useful as possible. Curious about what’s already done or currently in the works? Check the changelog for updates.
You've imported your data, connected your pipes, and allocated your demands — but how do you know if the information you've used to build your model is actually correct and reliable? This is where data validation and verification come in, and it's a step you absolutely can't afford to skip.
This section stresses the critical importance of this quality assurance phase, highlighting that even the most seemingly trustworthy data sources can, and often do, contain errors, inconsistencies, or outdated information. Without robust validation, you risk building a model on a shaky foundation, leading to questionable simulation results later on.
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